Interprets MLVI in SBMs as srGW OT projection, proves asymptotic consistency of unregularized srGW, and demonstrates regularized version for simultaneous parameter recovery and model selection.
Semi- relaxed gromov wasserstein divergence with applications on graphs.CoRR, abs/2110.02753, 2021
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A sliced IGW distance is introduced with closed-form 1D expressions, rotational invariance, and studied structural and computational properties for efficient data alignment.
GGDA framework generates knowledge-preserving intermediate graphs via FGW metric and a vertex-based progression to enable gradual domain adaptation across large graph distribution shifts.
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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models
Interprets MLVI in SBMs as srGW OT projection, proves asymptotic consistency of unregularized srGW, and demonstrates regularized version for simultaneous parameter recovery and model selection.
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Sliced Inner Product Gromov-Wasserstein Distances
A sliced IGW distance is introduced with closed-form 1D expressions, rotational invariance, and studied structural and computational properties for efficient data alignment.
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Gradual Domain Adaptation for Graph Learning
GGDA framework generates knowledge-preserving intermediate graphs via FGW metric and a vertex-based progression to enable gradual domain adaptation across large graph distribution shifts.